A new approach for selecting portfolio of new product development projects

The most decisive factor that survives enterprises under stiff competition is the development of new product (NPD), and when entering the product development stage after the fuzzy front end, a best project portfolio should be finalized in order to potentially create expected revenue and competitive advantage. However, even it reaches the end of the fuzzy front stage; the NPD project is still significantly involved with uncertainties, complexities and fuzziness. To assist R&D managers making decision in this environment, this study proposes a new approach which combines fuzzy set theory and multi-criteria group decision making method into a NPD project portfolio selection model. This model takes into account project performance, project delivery and project risk, and formulates the selection decision of NPD project portfolio as a fuzzy linear programming problem. The illustrative example shows that the model proposed can generate projects with the highest success rate under limited resources and manpower.     

A milp bi-objective model for static portfolio selection of R&;D projects with synergies

This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdependencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.     

基于战略对应的IT项目组合选择模型

把信息技术项目当作组合来管理可以通过平衡风险和收益来促进企业目标和IT应用的结合,但由于决策信息的不确定性和IT项目目标与企业战略的难以对应,企业面临IT项目组合选择的挑战。构建基于战略对应的IT项目组合选择模型,其中模糊集和模糊层次分析法用来刻画不确定信息和评估IT项目风险、成本及收益,关键成功因素法用来提高IT项目与企业战略的对应,并建立模糊0－1整数规划。利用定性可能性理论把模糊组合选择模型转化为一般可求解的整数规划形式,最后用一个案例说明模型的用法。     

Fuzzy multiattribute evaluation of R&D projects using a real options valuation model

R&D project selection decision is very important in two ways. First, in many organizations, R&D budget represents huge investment. Project selection decisions could be thought with the strategic objectives and plans of the firm. Second, R&D projects' organizational returns are multidimensional in nature and risky in terms of projected outcome. Real options approach helps to calculate this risky side of the selection process. This paper considers that multidimensional side of the R&D project selection process. Another consideration is the vagueness in the evaluation process. The fuzzy analytic hierarchy process, which takes monetary (fuzzy real option value) and nonmonetary (capability, success probability, trends, etc.) criteria into account, is used to make this selection among alternative R&D projects. A real case study is given to illustrate the application of the proposed approach. © 2008 Wiley Periodicals, Inc.     

A Lack of Insight: An Experimental Analysis of R&D Managers’ Decision Making in Innovation Portfolio Management

This study analyses the decision to exploit an innovation project and investigates differences in individuals’ evaluations of project attributes in the context of innovation project portfolio management. A conjoint field experiment was used to collect data on exploitation decisions made by 126 research and development (R&D) managers to test how managers evaluate specific project attributes in the context of innovation project portfolio management. I analyse the relative power and popularity of profitability, strategy, uncertainty and social dimensions of the portfolio while R&D managers exploit an innovation project. Moreover, using social judgement theory, I analyse actual exploitation processes (i.e., the innovation attributes an R&D manager considers while he or she is making an exploitation decision) and self‐reported decision‐making attributes (i.e., managers’ self‐reported data). The data underline that R&D managers value specific project attributes more and others less, and therefore find disparities in innovation project portfolio decision making. Based on this study's results, decision makers are better able to reflect and understand the influence of specific project attributes. Therefore, they should investigate established decision‐making processes which can help them to improve portfolio performance.     

Designing a hybrid system dynamic model for analyzing the impact of strategic alignment on project portfolio selection

One of the key challenges in project organizations is the alignment of portfolio management with major corporate strategies. Usually, project-based organizations use shared resources to control and plan the project portfolio. Therefore, the exploitation of shared resources and project planning decisions made in this regard can change the progress of projects and affect the success rate of the projects. In this article, the integration of system dynamics with multi-objective decision making is applied to address project portfolio selection. The project portfolio has been modeled using four basic dimensions including technology, complexity, innovation and time sensitivity. The aim is to plan and control the progress of project portfolio while maximizing the strategic adaptation subject to the changes of the human resources. For this purpose, a two-stage MO-PSO with TOPSIS is proposed for portfolio selection problem that can solve real-world instances of the problem in a reasonable time. The result of the sensitivity analysis indicated that the proposed decision support system (DSS) provides insights into the impact of strategic alignment on project portfolio selection. According to the simulation results, the integrated methodology of this research can assist in choosing the suitable projects to achieve a project's strategic goals following the organization strategy.     

An interactive algorithm for portfolio bi-criteria optimization of R&D projects in public organizations

In this paper we propose an interactive algorithm for the selection of portfolios of research and development (R&D) projects in public organizations based on a bi-criteria optimization model, the need for such a model arises when the decision maker (DM) does not trust enough on the portfolio quality measure. This algorithm efficiently exploits the structure and nature of the problem to support the DM. An interesting proposal is also the representation of a portfolio as a set of “rules of support/rejection”; in this way the DM can not only valuate the portfolio by its numerical measures but also compare against his/her beliefs in a way that is more natural for him, which also allows for supporting with more arguments the solution obtained so far. Rough set methodology is employed for rule discovering. The text was submitted by autors in English.     

Credibilistic multi-period portfolio optimization model with bankruptcy control and affine recourse

Avoiding the possibility of bankruptcy during the investment horizon is very important to multi-period portfolio management. This paper considers a multi-period fuzzy portfolio selection problem with bankruptcy control. A multi-period portfolio optimization model imposed by a bankruptcy control constraint in fuzzy environment is proposed on the basis of credibility theory. In the proposed model, a linearly recourse policy is used to reflect the influence of historical predication basis on current portfolio decision. Three optimization objectives, viz., maximizing the terminal wealth and minimizing the cumulative risk and the cumulative uncertainty of the returns of portfolios over the whole investment horizon, are taken into consideration. For solving the proposed model, a fuzzy programming approach is applied to transform it into a single objective programming model. Then, a hybrid particle swarm optimization algorithm is designed for solution. Finally, an empirical example is presented to illustrate the application of the proposed model and solution comparisons are also given to demonstrate the effectiveness of the designed algorithm.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty

This paper deals with the problems of both project valuation and portfolio selection under the assumption that the investment capitals and the net cash flows of the projects are fuzzy variables. Using the credibilistic expected value and the credibilistic lower semivariance of fuzzy variables, this paper proposes both the credibilistic return index and the credibilistic risk index, which are measures of investment return and investment risk with annuity form for evaluating single project. Moreover, a composite risk-return index for selecting the optimal investment strategy is also presented. Then, we set up a general project portfolio optimization model with fuzzy returns and two specific models: triangle and interval fuzzy returns. Furthermore, we provide two algorithms: the improved heuristic rules based on genetic algorithm and the traversal algorithm. Finally, two numerical examples are presented to illustrate the efficiency and the effectiveness of these proposed optimization methods.     

不确定资源约束下项目鲁棒性调度算法*

在跨企业项目中,由于资源可用时间具有不确定性,从而使得项目计划具有易变性。针对这一问题,首先采用模糊集对项目的不精确时间参数和资源不确定性进行了表示,并在同时考虑调度的质量鲁棒性和解的鲁棒性情况下,定义了调度的鲁棒性度量,进而开发了遗传算法来求解不确定资源约束下的项目鲁棒性调度问题。最后,给出了应用实例,并通过仿真分析说明算法的有效性。该算法已被应用到跨企业项目管理系统中,获得了良好的效果。     

Simultaneous resource portfolio planning under demand and technology uncertainty in the semiconductor testing industry

One of the most challenging issues for the semiconductor testing industry is how to deal with capacity planning and resource allocation simultaneously under demand and technology uncertainty. In addition, capacity planners require a tradeoff among the costs of resources with different processing technologies, while simultaneously considering resources to manufacture products. The need for exploring better solutions further increases the complexity of the problem. This study focuses on the decisions pertaining to (i) the simultaneous resource portfolio/investment and allocation plan accounting for the hedging tradeoff between the expected profit and risk, (ii) the most profitable orders from pending ones in each time bucket under demand and technology uncertainty, (iii) the algorithm to efficiently solve the stochastic and mixed integer programming problem. Due to the high computational complexity of the problem, this study develops a constraint-satisfaction based genetic algorithm, in conjunction with a chromosome-repair mechanism and sampling procedure, to resolve the above issues simultaneously. The experimental results indicate that the proposed mathematical model can accurately represent the resource portfolio planning problem of the semiconductor testing industry, and the solution algorithm can solve the problem efficiently.     

Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty

Absolute deviation is a commonly used risk measure, which has attracted more attentions in portfolio optimization. The existing mean-absolute deviation models are devoted to either stochastic portfolio optimization or fuzzy one. However, practical investment decision problems often involve the mixture of randomness and fuzziness such as stochastic returns with fuzzy information. Thus it is necessary to model portfolio selection problem in such a hybrid uncertain environment. In this paper, we employ random fuzzy variable to describe the stochastic return on individual security with ambiguous information. We first define the absolute deviation of random fuzzy variable and then employ it as risk measure to formulate mean-absolute deviation portfolio optimization models. To find the optimal portfolio, we design random fuzzy simulation and simulation-based genetic algorithm to solve the proposed models. Finally, a numerical example for synthetic data is presented to illustrate the validity of the method.     

Fuzzy mean–variance–skewness portfolio selection models by interval analysis

In portfolio selection problem, the expected return, risk, liquidity etc. cannot be predicted precisely. The investor generally makes his portfolio decision according to his experience and his economic wisdom. So, deterministic portfolio selection is not a good choice for the investor. In most of the recent works on this problem, fuzzy set theory is widely used to model the problem in uncertain environments. This paper utilizes the concept of interval numbers in fuzzy set theory to extend the classical mean–variance (MV) portfolio selection model into mean–variance–skewness (MVS) model with consideration of transaction cost. In addition, some other criteria like short and long term returns, liquidity, dividends, number of assets in the portfolio and the maximum and minimum allowable capital invested in stocks of any selected company are considered. Three different models have been proposed by defining the future financial market optimistically, pessimistically and in the combined form to model the fuzzy MVS portfolio selection problem. In order to solve the models, fuzzy simulation (FS) and elitist genetic algorithm (EGA) are integrated to produce a more powerful and effective hybrid intelligence algorithm (HIA). Finally, our approaches are tested on a set of stock data from Bombay Stock Exchange (BSE).     

考虑投资者心理特征的模糊投资组合优化模型

投资者在实际金融市场中的决策行为往往会受到主观心理认知的影响.考虑参照依赖、敏感性递减和损失厌恶等影响投资决策的心理特征,研究模糊环境下的投资组合选择问题.首先,假设资产的收益为梯形模糊数,依据前景理论中的价值函数,将组合收益转化为体现投资者心理特征的感知价值;然后,以感知价值的可能性均值最大化和可能性下半方差最小化为目标,建立考虑心理特征的模糊投资组合优化模型;接着,为了有效地求解模型,设计一个多种群遗传算法;最后,通过实例分析表明模型和算法的有效性.结果表明,与传统的遗传算法相比,所设计的多种群遗传算法可更有效地求解模型,考虑心理特征的模糊投资组合优化模型能够提升投资者的满意程度,可为实际的投资活动提供决策支持.     

A modular Decision Support System for optimum investment selection in presence of uncertainty: Combination of fuzzy mathematical programming and fuzzy rule based system

In this paper, we have developed a modular Decision Support System (DSS) in order to select an optimum portfolio of several chances for investments in presence of uncertainty. The investments are considered as the projects so as their initial investment costs, profits, resource requirement, and total available budget are assumed to be uncertain. This uncertainty has been modeled using fuzzy concepts. The proposed DSS has two main modules. The first one is a fuzzy binary programming model which represents the mathematical model of the associated fuzzy capital-budgeting problem. It involves finding optimum combination of investment portfolio considering a multi-objective measurement function and subject to several set of constraints. The results of optimistic and pessimistic analysis of the aforementioned fuzzy binary programming model plus a managerial Confidence Level (CL) value are treated as input of a fuzzy rule based system which is the second module of the proposed DSS. Although some projects are simple to make a decision about at the final step of the first module but the unique output of the second module of the proposed DSS is Risk of Investment (ROI) for all remained project. The logic relations between precedence parts of the rules as well as CL value will work in favor of computational efforts in second module through diminishing some unessential rules. This will help to define a complete set of fuzzy IF-THEN rules more efficiently. The proposed DSS can help the decision makers to select an optimum investment portfolio with minimum risk in a complete ambiguous condition.     

Partner selection model and soft computing approach for dynamic alliance of enterprises

Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0-1 integer programming with non-analytical objective function. Then, the solution space is reduced by defining the inefficient candidate. By using the fuzzy rule quantification method, a fuzzy logic based decision making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded genetic algorithm. We compare the algorithm with tranditional methods. The results show that the suggested approach can quickly achieve optimal solution for large size problems with high probability. The approach was applied to the partner selection problem of a coal fire power station construction project. The satisfactory results have been achieved.     

Finding research trend of convergence technology based on Korean R&D network

Traditionally, collaboration network or citation network is used to answer the old question how scientists or engineers interact with each other. This paper introduces a R&D network to make up the missing aspect of the traditional approaches about using multi-sources and to find out the trend of convergence technology R&D in Korea. We collect data about human resources and national R&D projects from Korean national R&D databases, and then construct a weighted network between experts by using meta-data mapping and the network folding technique. And we apply Newman’s grouping algorithm that is generalized to a weighted network for detecting the community structure of the network. Gathering data from multi-sources is useful to reveal the structure of network rather than to use only one database. Lastly, we perform a network analysis to examine important experts. The result shows significant information about research trend and core experts in Korea. We expect this study will be helpful in three ways: (1) how to make a network from heterogeneous multi-sources, (2) how to figure out the current situation of convergence technology R&D, (3) how to discover who are important people in Korean convergence technology R&D network. And this paper is just a cornerstone of the work to investigate the current situation of national R&D projects in Korea.     

Real Options and Incremental Search in Pharmaceutical R&D Project Portfolio Management

This paper investigates the role of real options reasoning in R&D project portfolio management and investment decisions of pharmaceutical firms. We analyse a unique dataset that integrates information on initiation and termination of clinical trials at the level of specific medical indications. Consistent with existing literature, we find a positive relationship between market size and firm entry in clinical trials. We also show that the option value of R&D investments, as proxied by the scope of R&D projects, affects the selection of target markets. Moreover, high‐risk research areas attract more entry, in line with the predictions of real options theory. However, we also find that more flexibility in project duration and delayed project discontinuation attract higher rates of entry. Departures from pure real options reasoning are motivated by the presence of incremental learning in pharmaceutical R&D.     

Valuation of R&;D Sequential Exchange Options Using Monte Carlo Approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlo method. R&D projects generally involve multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options’ in which investments present uncertainty both in the gross project value and in their costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to evaluate the European and American Real Compound exchange options through Monte Carlo simulations. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.